I’ve had the joy and privilege to observe closely the trajectory of one of the most inspired entrepreneurs and company builders in the world, Brazilian Jorge Paulo Lemann. Lemann and his two partners, Marcel Herrmann Telles and Carlos Alberto Sicupira, started with a tiny brokerage firm and from there built one of the most successful investment banks in Latin America. And while the partners were smart and accomplished at managing money, they discovered they had a peculiar genius for building a culture filled with hungry, fanatic people who thrived on meritocracy. They became so good at building culture that they eventually began to consider buying entire companies outright and then running them based on their cultural operating system with the goal to build and grow them forever. “If we believe in our culture,” they said to themselves, “why not bet big on it?

These two features of technology revolutions—their promise to deliver progress and their tendency instead to reverse it for people out of power, especially the most vulnerable—are perhaps truer than ever for the moment we now find ourselves in with artificial intelligence. Since its conception, the development and use of AI has been propelled by tantalizing dreams of modernity and shaped by a narrow elite with the money and influence to bring forth their conception of the technology. That conception is what has led to the exploding social, labor, and environmental costs that are playing out around the world today, particularly, as we’ll see, in many Global South countries, for which the consequences of their dispossession by historical empires still linger in delayed economic development and weaker political institutions. And yet, just like the South Carolina congressman, Silicon Valley has painted the experiences of those being exploited and harmed by the technology as happier because of it.

That “freak coincidence” revealed a disturbing formula. When faced with economic collapse, Venezuela suddenly checked off the perfect mix of conditions for which to find an inexhaustible supply of cheap labor: Its population had a high level of education, good internet connectivity, and, now, a zealous desire to work for whatever wages. It was not the only country that fit that description. More populations were getting wired to better internet. And with accelerating climate change and growing geopolitical instability, it was hard to bet against more populations plunging into crisis. “It’s quite likely there will be another Venezuela,” Schmidt said.

Scale proceeded to repeat the playbook it had developed in Venezuela again and again. It offered high earnings in each new market to attract workers and throttled those earnings as it settled in. It tinkered with the size of its payouts to taskers through rounds of experimentation that full-time employees, sitting in its now 180,000-square-foot San Francisco headquarters, discussed as optimization and innovation. Workers meanwhile saw their livelihoods decimated with the unpredictable changes. The Scale spokesperson said the company rejected the characterization that it has targeted economies in hardship and purposely cut back earnings.

Scale downgraded Kenya to a Group 5 designation: blacklisted. There was also another reason to exit Kenya. By then, Scale was moving on to a new focus, following the demands of the AI industry. OpenAI and its competitors were increasingly searching for highly educated workers to perform RLHF—doctors, coders, physicists, people with PhDs. So went the profit-chasing progression of chatbot development. Those willing to pay money for chatbots were not casual consumers but businesses that expected tools to perform complex tasks such as in science and software development. Kenya did not fulfill the new labor demand. Scale was now recruiting a fresh workforce primarily in the US with a new worker-facing platform called Outlier, offering as much as forty dollars an hour.

It was yet another stark illustration of the logic of AI empires. Behind promises of their technologies enhancing productivity, unlocking economic freedom, and creating new jobs that would ameliorate automation, the present-day reality has been the opposite. Companies pad their bottom lines, while the most economically vulnerable lose out and more and more highly educated people become ventriloquists for chatbots.

The empire’s devaluing of the human labor that serves it is also just a canary: It foretells how the technologies produced atop this logic will devalue the labor of everyone else. In fact, for the artists, writers, and coders whose labor the empires of AI turned into free training data, that is already happening.

Scale’s decision would send Winnie and her family spiraling. By then Millicent had lost her job and Remotasks had been the only thing keeping them afloat. Now they were struggling to feed their kids. Winnie was terrified they would soon be evicted. In her inbox, the email Scale sent to inform workers of the shutdown was cold and clinical: “We are discontinuing operations in your current location,” it read. “You have been off-boarded from your current project.”